Personal authentication method and device

ABSTRACT

During registration, an iris image taken is frequency-analyzed using a plurality of frequencies f 1  to fn to generate features for the respective frequencies f 1  to fn. During authentication, frequencies f 2  to fn are selected from the frequencies f 1  to fn for use in frequency analysis for authentication. An iris image of a person to be authenticated taken is frequency-analyzed using the selected frequencies f 2  to fn to generate features for the respective frequencies f 2  to fn. The generated features are compared with the features generated for the same frequencies during the registration to perform personal authentication.

BACKGROUND OF THE INVENTION

[0001] The present invention relates to a technology of personalauthentication in which features are obtained from biologicalinformation by frequency analysis and personal authentication isperformed based on the similarity between the features.

[0002] As individual identification techniques using human biologicalinformation (biometrics), there have been proposed many methods usinginformation of fingerprints, irises, blood-vessel arrangements in theretinas, faces, and the like. Among these, individual identificationusing an iris, in particular, is expected to be mainstream of biometricsauthentication in the future for the reasons that:

[0003] (1) an iris pattern can be acquired with a camera in a noncontactmanner,

[0004] (2) the false acceptance rate (FAR) is significantly low due tocomplexity of the iris pattern, and

[0005] (3) the iris pattern remains unchanged substantially through thelife of the owner.

[0006] Techniques for extracting iris features from iris images andidentifying individuals are disclosed in U.S. Pat. No. 5,291,560,Japanese National Phase PCT Laid-Open Publication No. 8-504979, and“High Confidence Visual Recognition of Persons by a Test of StatisticalIndependence”, IEEE Transactions on Pattern Analysis and MachineIntelligence, Vol. 15, No. 11, Nov. 1993 (these three disclosures areroughly the same in contents).

[0007] In the above techniques, an iris image is analyzed at multipleresolutions using multi-scale self-similarity type two-dimensionalquadrature band-pass filters (Gabor filters, for example) to generate afeature (iris code). To state as a specific procedure, a digitized imageof a human eye is captured with a video camera, and the boundary betweenthe iris and the sclera and the boundary between the iris and the pupilare determined to separate an iris region from others. A polarcoordinate system is applied to the separated iris image, and aplurality of ring analysis bands are determined. Analysis and coding arethen performed for the analysis bands using a signal processor comprisedof multi-scale quadrature band-pass filters. The thus-generated iriscodes are compared with each other by calculating a hamming distancebetween the codes as shown in FIG. 24, to determine whether or not thecompared iris codes originate from an identical person.

[0008] Problems to be Solved

[0009] The above technique is based on the premise that multi-scalefrequency analysis is performed for predetermined fixed frequency bandsusing an image capture device providing a predetermined fixed resolutionin both cases of iris registration and comparison. To fulfill thispremise, dedicated registration and authentication devices arenecessary.

[0010] In view of the recent sophistication in function of cellularphones and personal digital assistants (PDAs), increase in capacity ofcommunication bands, and the like, the following use of personalauthentication is considered possible in the near future. That is, acellular phone or PDA equipped with an image capture device (camera) maybe used for taking an iris image of a person and authenticating theperson. And this capability may be utilized in occasions of accesscontrol, such as logging in to a cellular phone or PDA, authenticationin electronic commerce (EC), control of entrance/exit into/from a placerequiring physical security, and alternative use to a key of a house.When the above occasions are to be realized, an image capture deviceincorporated in or mounted externally on a cellular phone or PDA willpossibly be comparatively low in resolution at the beginning. Inaddition, specifications for image capture devices will possibly bedifferent among the types of the devices. Moreover, it is consideredthat authentication will be effected via a variety of apparatuses suchas a terminal mounted on the door, not only a cellular phone and a PDA.

[0011] As described above, an iris image may be taken with a variety ofapparatuses providing lower to higher resolutions during authentication.Under this situation, if frequency analysis is performed at fixedfrequency bands by the conventional method described above, thefollowing problem will arise. That is, when a low-resolution image isinput, a part of a feature obtained by analysis at a high frequency(specifically, a frequency component equal to or higher than Fs/2 whereFs is a sampling frequency) is no more useful as the feature. Therefore,if this part obtained by high-frequency analysis is counted as part ofthe feature, the entire correlation value decreases and thusauthentication precision possibly degrades.

SUMMARY OF THE INVENTION

[0012] The object of the present invention is providing personalauthentication method and device using biological information, capableof suppressing degradation in authentication precision to maintainadequate authentication precision even in a future environment of usinga variety of apparatuses for authentication, for example.

[0013] To state specifically, the present invention is directed to apersonal authentication method using biological information. Accordingto the method, during registration, acquired biological information isfrequency-analyzed using a plurality of frequencies to generate afeature for each frequency and register the feature, and the methodincludes the steps of: selecting a frequency used for frequency analysisfor authentication from the plurality of frequencies; performingfrequency analysis for acquired biological information of a person to beauthenticated using the selected frequency to generate a feature for thefrequency; and comparing the generated feature with the featuregenerated for the same frequency during the registration to performpersonal authentication.

[0014] According to the invention described above, a frequency used forfrequency analysis during authentication is selected from a plurality offrequencies used for frequency analysis during registration. By thisselection, a frequency component that may possibly reduce the entirecorrelation value and is unwanted from the aspect of authenticationprecision, for example, can be eliminated. This suppresses degradationin authentication precision.

[0015] The biological information is preferably an image of an iris ofan eye.

[0016] The selection of the frequency during the authentication in thepersonal authentication method of the present invention is preferablyperformed based on a resolution of an iris image taken during theauthentication. The resolution of the iris image is preferablydetermined from the iris image itself. Alternatively, it may bedetermined based on the length of a circumference corresponding to theboundary between the iris and the pupil of the iris image, or may bedetermined from information on an apparatus with which the iris imagewas taken.

[0017] The selection of the frequency during the authentication in thepersonal authentication method of the present invention is preferablyperformed based on authentication precision for each combination of theplurality of frequencies. The authentication precision is preferablycalculated using a distribution of authentication scores (ex. distances)between identical persons and a distribution of authentication scoresbetween different persons.

[0018] The authentication precision during the authentication in thepersonal authentication method of the present invention is preferablyestimated from the selected frequency. The authentication precision maybe estimated using a distribution of authentication distances betweenidentical persons and a distribution of authentication distances betweendifferent persons. Whether or not the person to be authenticated shouldbe finally authenticated may be judged according to the estimatedauthentication precision. A right to be bestowed on the person to beauthenticated after the authentication may be controlled, or whether ornot re-authentication is performed may be judged, according to theestimated authentication precision.

[0019] In another aspect, the present invention is directed to apersonal authentication device using biological information. Duringregistration, acquired biological information is frequency-analyzedusing a plurality of frequencies to generate a feature for eachfrequency and register the feature, and the device includes: means forselecting a frequency used for frequency analysis for authenticationfrom the plurality of frequencies; means for performing frequencyanalysis for acquired biological information of a person to beauthenticated using the selected frequency to generate a feature for thefrequency; and means for comparing the generated feature with thefeature generated for the same frequency during the registration toperform personal authentication.

[0020] The biological information is preferably an image of an iris ofan eye.

BRIEF DESCRIPTION OF THE DRAWINGS

[0021]FIG. 1 is a conceptual illustration of technical features of apersonal authentication method of Embodiment 1 of the present invention.

[0022]FIG. 2 is a schematic block diagram of a personal authenticationsystem of the embodiments of the present invention.

[0023]FIG. 3 is an illustration of an appearance of a camera-equippedcellular phone as an iris authentication terminal.

[0024]FIG. 4 is a block diagram of an internal configuration of the irisauthentication terminal.

[0025]FIG. 5 is a flowchart of processing during registration in thepersonal authentication method of Embodiment 1 of the present invention.

[0026]FIG. 6 is a flowchart of processing during authentication in thepersonal authentication method of Embodiment 1 of the present invention.

[0027]FIG. 7 is an illustration of a structure of an iris.

[0028]FIG. 8 is a view representing an iris pattern in terms of thefrequency range using Fourier transform.

[0029]FIG. 9 is a view illustrating frequency analysis using four Gaborfilters.

[0030]FIG. 10 represents Gaussian function used for the Gabor filter.

[0031]FIGS. 11A, 11B and 11C illustrate an example of an iris code.

[0032]FIG. 12 is an illustration of generation of a registration iriscode.

[0033]FIG. 13 is an illustration of generation of an identification iriscode.

[0034]FIG. 14 is an illustration of comparison between the registrationiris code and the identification iris code.

[0035]FIG. 15 is an illustration of calculation of a hamming distance.

[0036]FIG. 16 is a conceptual illustration of distributions ofauthentication distances between identical persons and between differentpersons.

[0037]FIG. 17 is a flowchart of processing in a personal authenticationmethod in Embodiment 2 of the present invention.

[0038]FIGS. 18A and 18B are conceptual illustrations of distributions ofauthentication distances between identical persons and between differentpersons.

[0039]FIG. 19 is an illustration of a method for generating a pluralityof iris images with different resolutions using low-pass filters.

[0040]FIG. 20 is an illustration of selection of Gabor filters.

[0041]FIGS. 21A to 21D are views representing the images generated usinglow-pass filters in terms of the frequency range using Fouriertransform.

[0042]FIG. 22 is an illustration of selection of Gabor filters.

[0043]FIG. 23 is an illustration of iris DBs in which extracted featuresare stored separately by combination of analysis frequency bands.

[0044]FIG. 24 is an illustration of conventional personalauthentication.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0045] Hereinafter, preferred embodiments of the present invention willbe described with reference to the accompanying drawings. In thefollowing description, iris authentication, that is, personalauthentication using an image of an iris of an eye, is exemplified. Itshould however be noted that the present invention is also applicable toother types of personal authentication using other biologicalinformation such as a fingerprint and a voiceprint.

[0046] Embodiment 1

[0047]FIG. 1 is a conceptual illustration of technical features of apersonal authentication method of Embodiment 1 of the present invention.As shown in FIG. 1, in this embodiment, during registration, frequencyanalysis at a plurality of frequencies f1 to fn is performed for an irisimage 1 taken, and features are generated for the respective frequenciesf1 to fn. During authentication, the frequencies f2 to fn among theplurality of frequencies f1 to fn used during the registration areselected for use in frequency analysis for authentication based on theresolution of an iris image 2 taken. Frequency analysis at the selectedfrequencies f2 to fn is performed for the iris image 2 of a person to beauthenticated, to generate features for the respective frequencies f2 tofn. The generated respective features are compared with those for thesame frequencies obtained during the registration, to perform personalauthentication.

[0048] By adopting the method described above, even when an iris imageof a person to be authenticated is taken with an image capture deviceproviding a resolution lower than that used during the registration,personal authentication can be performed using this lower-resolutioniris image. In addition, since a feature for a non-selected frequency,which is useless as a feature, is eliminated, degradation inauthentication precision can be suppressed in the personalauthentication.

[0049]FIG. 2 is a schematic diagram of a personal authentication systemof this embodiment. Referring to FIG. 2, an iris authentication server11 has an iris database (DB) 12 storing iris data of a plurality ofpersons and is connected to a network 13 such as the Internet, privatelines and public lines. At least one Iris registration device 14 and atleast one iris authentication terminal 15, which are provided with animage capture device, are connected to the network 13.

[0050]FIG. 3 is an illustration of a camera-equipped cellular phone 21applied as the iris authentication terminal 15 of the irisauthentication system shown in FIG. 2. FIG. 4 is a block diagram of aninner configuration of the iris authentication terminal 15.

[0051] A plurality of iris authentication servers 11 may be provided,for placement in each area or organization using the network and/or forplacement of mirror servers for dispersing the load. The iris DB 12 maybe connected to the iris authentication server via the network.

[0052] The function of the iris registration device 14 may beincorporated in the iris authentication server 11, or the irisauthentication terminal 15 may have both functions of registration andauthentication. When a plurality of iris authentication terminals 15 areprovided, the image capture specifications for the terminals are notnecessarily the same.

[0053]FIGS. 5 and 6 are flowcharts of processing during registration andthat during authentication, respectively, in the personal authenticationmethod of this embodiment. Hereinafter, the processing duringregistration and that during authentication in the personalauthentication method of this embodiment will be described separately.

[0054] <Registration>

[0055] During registration, in step SA01 for image capture, an irisimage is taken with the iris registration device 14 at a “resolution”adequate to capture fine details of the iris pattern. The “resolution”as used herein refers to “how finely the iris was sampled”, which mayalso be called the resolving power. An image capture device/lens systemincorporated in the iris registration device 14 should be the onecapable of taking an iris image at an adequate resolution.

[0056]FIG. 7 illustrates a structure of an iris. Referring to FIG. 7, aniris 41 mainly includes folds 42 (a pattern extending radially fromaround the boundary with a pupil 45), iris pits 43, an iris frill 44,and the like. The pattern of the iris 41 differs among individuals.Personal authentication is realized using this difference in pattern, inwhich a feature is extracted from the pattern of the iris 41 and theextracted feature is compared with others.

[0057]FIG. 8 is a view representing the iris pattern in terms of thefrequency range using Fourier transform. Although 2D Fourier transformis actually used since the actual iris pattern is a two-dimensionalimage, one-dimensional representation is shown in FIG. 8 forsimplification of description. The one dimension in this case is adimension in the circumferential direction of a two-dimensional irispattern obtained by separating from other portions in a manner describedlater and applying polar coordinates. That is, to state differently, aradial pattern of the iris such as the pattern of the folds 42 isfrequency-analyzed in the circumferential direction. The reason why thefrequency analysis is performed in the circumferential direction is thatthe radial pattern of the iris is known important for personalauthentication.

[0058] In this embodiment, the frequency of a sine wave of which oneperiod covers 360 degrees in a circumferential direction (one cycle) isexpressed as frequency 1 (the unit of the frequency is Hz, which is alsoapplicable to the rest of the description).

[0059] In FIG. 8, Fm represents the upper-limit frequency of a frequencyband effective in use of the iris pattern for authentication. Accordingto the sampling theorem, in order to use the upper-limit frequency Fmfor analysis of the iris pattern, an iris image should be captured at asampling frequency Fs=Fm×2 or higher. By this capture, the “adequateresolution” described above is obtained. Alternatively, the upper-limitfrequency Fm may be determined by capturing iris images at varioussampling frequencies Fs in preliminary experiments and selecting asampling frequency Fs at which the highest recognition performance isexhibited. Otherwise, the upper-limit frequency Fm may be estimated fromgaps between the finest wrinkles observable obtained by observing irispatterns of various persons.

[0060] The iris image taken during the registration is sent to the irisauthentication server 11 via the network 13, together with the ID of theperson concerned obtained separately. Note that if there is available aniris authentication terminal 15 provided with the ability of taking aniris image at an adequate resolution, registration of an iris image canalso be performed using such a terminal 15.

[0061] In steps SA02 to SA07, a feature (iris code) used forauthentication is extracted from the iris image taken. Note thatalthough the iris authentication server 11 performs the extraction of afeature in this embodiment, the iris registration device 14 may performa series of processing from capturing of an iris image throughextraction of a feature, and send the generated iris code via thenetwork 13 to be stored in the iris DB 12.

[0062] Steps SA02 to SA05 can be implemented by any method as long asthe iris region can be extracted stably. In this embodiment, the methoddescribed in Japanese National Phase PCT Laid-Open Publication No.8-504979 is employed. Details of this method are omitted here, and onlythe outline is described as follows.

[0063] In step SA02, the inner boundary of the iris is first determinedfor extraction of the iris region. More specifically, utilizing the factthat the brightness is different between the pupil and the iris, acircle in which the integral of the brightness abruptly changes on thecircumference is sought among circles having regularly increasing radii,and the center (x0, y0) and the radius r0 of the circle are obtained.

[0064] Likewise, in step SA03, the outer boundary of the iris isdetermined. In this step, also, the fact that the brightness isdifferent between the iris and the sclera is utilized. In this case,since the boundary between the iris and the sclera is often occluded bythe upper eyelid and the lower eyelid, calculation of the integral ofthe brightness is made for only the right and left arc portions (called“pie wedges” in the above publication) excluding the upper and lowerportions of the circle. In other words, a circle in which the integralof the brightness at the pie wedges abruptly changes is sought amongcircles having regularly increasing radii, and the center (x1, y1) andthe radius r1 of the circle are obtained.

[0065] Thus, from steps SA02 and SA03, the inner and outer boundaries ofthe iris are determined, and thus the iris region is extracted.

[0066] In step SA04, a polar coordinate system is applied to theextracted iris region. The origin of the polar coordinate system is setat the center (x0, y0) of the pupil. In the dimension in the radialdirection, the circumference of the pupil (that is, the innermost radiusof the iris) is determined as radius 0 while the circumference of theiris (that is, the outermost radius of the iris) is determined asradius 1. In-between values of the radius are linearly interpolatedbetween 0 and 1 depending on the distance from the pupil circumferenceto the iris circumference. In the dimension in the angular direction,values between 0 and 360 degrees are determined. Thus, although the sizeof the iris image varies with the difference in iris size amongindividuals, the zoom value of a camera, the distance between the cameraand the iris, and the like, extraction of a feature from the iris imagecan be performed without being influenced by the iris size by settingthe polar coordinate system described above.

[0067] In step SA05, the iris region is divided into a plurality ofconcentric ring regions (for example, eight regions).

[0068] In step SA06, as disclosed in the above publication, the polarcoordinates-applied iris image is subjected to 2D Gabor filters, whichare multi-scale band-pass filters. The Gabor filter is represented bythe following expression.

G(r,θ)=e ^(2πjω(θ−θ) ^(₀) ⁾ e ^(−(r−r) ^(₀) ⁾ ² ^(/α) ² e ^(−(θ−θ) ^(₀)^()/β) ²   (1)

[0069] where r is the radius, θ is a radial angle, ω is the angularfrequency, and α and β are constants. The parameters α and β varyinversely with the angular frequency ω. Herein, a plurality of angularfrequencies ω are prepared, and analysis of the iris image is performedusing a plurality of Gabor filters corresponding to the respectiveangular frequencies ω.

[0070]FIG. 9 illustrates analysis using four frequencies. In FIG. 9, thex-axis represents the frequency, showing how the frequency band fromfrequency 0 (DC component) to the upper-limit frequency Fm is analyzedwith four Gabor filters having different passbands. F1 to F4 denote thecenter frequencies of the passbands of the four Gabor filters. Whenexpressed in terms of the angular frequencies described above, thefollowing are established.

[0071] ω1=2πF1, ω2=2πF2, ω3=2πF3, ω4=2πF4

[0072] Note that the upper-limit frequency Fm and the upper limit F4U ofthe passband of the Gabor filter are not necessarily identical to eachother as shown in FIG. 9, but may establish the relationship F4U≦Fm.

[0073] In FIG. 9, the passband of each Gabor filter is defined asbetween points at which the peak value of the Gaussian function used inthe Gabor filter is halved, as shown in FIG. 10. To state differently,if the fringes of the Gaussian function (portions apart from the center)are taken into consideration, the passbands of the Gabor filters overlapeach other. The points at which the passbands of the Gabor filters meetmay be set at points other than those at which the Gaussian function ishalved, such as points of σ, 2σ or 3σ where σ is the standard deviationof the Gaussian function.

[0074] A feature is then extracted from a Gabor-filtered signal asdisclosed in the above publication. That is, the signal is changed to abinary value determined by whether the output of the Gabor filter ispositive or negative and coded, as represented by the followingexpressions.

MSB _(Re)(r,θ)=1ifRe∫ _(ρ)∫_(φ) e ^(2πjω(θ−φ)) e ^(−(r−ρ)) ² ^(/α) ² e^(−(θ−φ)/β) ² I(ρ,φ)ρdρdφ>0

MSB _(Re)(r,θ)=0ifRe∫ _(ρ)∫_(φ) e ^(2πjω(θ−φ)) e ^(−(r−ρ)) ² ^(/α) ² e^(−(θ−φ)/β) ² I(ρ,φ)ρdρdφ≦0

MSB _(Im)(r,θ)=1ifIm∫ _(ρ)∫_(φ) e ^(2πjω(θ−φ)) e ^(−(r−ρ)) ² ^(/α) ² e^(−(θ−φ)/β) ² I(ρ,φ)ρdρdφ>0

MSB _(Im)(r,θ)=0ifIm∫ _(ρ)∫_(φ) e ^(2πjω(θ−φ)) e ^(−(r−ρ)) ² ^(/α) ² e^(−(θ−φ)/β) ² I(ρ,φ)ρdρdφ≦0

[0075] where I(ρ,θ) is an input iris image represented by the polarcoordinate system.

[0076]FIGS. 11A to 11C illustrate an example of extraction of an iriscode. An iris image is actually a two-dimensional signal (in the radialdirection and the angular direction (circumferential direction) on thepolar coordinate system). However, in these figures, the iris image isshown one-dimensionally in the angular direction for simplification ofdescription. FIG. 11A illustrates an original signal representing thebrightness along the circumference of a circle having a certain radiusof the iris image, FIG. 11B illustrates a signal obtained byGabor-filtering the original signal of FIG. 11A, and FIG. 11Cillustrates a signal obtained by changing the signal of FIG. 11B to abinary value, that is, an iris code. The iris code shown in FIG. 11C isgenerated for each frequency every ring region obtained by the divisionin step SA05.

[0077] As shown in FIG. 12, sub-features Fsi obtained from analysis atrespective frequencies Fi are put together to obtain a registration iriscode FT. In step SA07, the registration iris code FT and analysisfrequency information are stored in the iris DB 12 in association withthe ID of the person concerned.

[0078] In this embodiment, the polar coordinate system was adopted forthe iris image. Alternatively, a normal coordinate system may beadopted. Multi-resolution frequency analysis using Gabor filters, a typeof multi-scale self-similarity type two-dimensional band-pass filters,was performed. Alternatively, another method utilizing multi-resolutionanalysis, such as wavelet analysis, may be employed.

[0079] In this embodiment, the feature was calculated using the realpart and imaginary part of the Gabor filter separately. Alternatively,the power obtained by calculating the square root of sum of squares ofthe two parts may be used as the feature. The output of the Gabor filtermay not be changed to a binary value, but the multilevel output may beused as it is as the feature.

[0080] <Authentication>

[0081] During authentication, the following processing is performed.

[0082] First, in step SB01 for image capture in the flowchart of FIG. 6,the user intending to receive authentication, that is, the person to beauthenticated inputs an iris image using the cellular phone 21 shown inFIGS. 3 and 4, for example. In more detail, the user examines an imagecurrently captured by a camera 22 (an image capture element incorporatedin the camera 22) displayed on a monitor 23, and presses a button 25 forexecution of image capture when it is confirmed that his or her own irisimage on the monitor 23 is in sharp focus. Upon the pressing of thebutton, an illuminator 24 is lit, and an image is taken insynchronization with the lighting timing. The iris image taken is storedin a frame memory 32 of the cellular phone 21. Thereafter, an irisfeature extraction program stored in a program memory 34 is readtherefrom into a work memory 33 for execution of step SB02 and thesubsequent steps in FIG. 6.

[0083] Although the camera-equipped cellular phone was used in the abovedescription, any other apparatus may be used as long as it is equippedwith a camera. For example, a camera-equipped PHS (personal handyphone), a camera-equipped PDA, a camera-equipped interphone, a digitalcamera having communication function, and the like may be used.

[0084] In this embodiment, feature extraction is performed by softwareusing an iris feature extraction program. Alternatively, iris featureextraction processing may be implemented by a dedicated circuit or adigital signal processor (DSP) to perform the feature extraction byhardware.

[0085] Steps SB02 and SB03 in which the iris region is defined from theiris image taken, step SB04 in which the coordinate system isdetermined, and step SB05 in which the iris region is divided intoanalysis regions are the same as steps SA02 to SA05 during theregistration, respectively. Description of these steps is thereforeomitted here.

[0086] In step SB06, the resolution of the iris image is calculated.Herein, a resolution R is defined as the circumferential length at theiris/pupil boundary or the iris inner boundary calculated in step SB02.The resolution R may be the number of pixels on the iris inner boundary,or may be determined as R=2πr0 from the radius r0 calculated in stepSB02.

[0087] In step SB07, the frequency band for analysis (analysis frequencyband) is determined from the resolution R of the iris image. Theresolution R is identical to the sampling frequency Fsp=R (Hz) in thecircumferential direction at the iris/pupil boundary. When R points aresampled for one period (360 degrees), the sampling period (samplinginterval) is 360/R degrees. Therefore, since sampling is made around 360degrees at sampling intervals of 360/R degrees, the sampling frequencyFsp=360/(360/R)=R (Hz).

[0088] Thus, according to the sampling theorem, the upper limit Fmp ofthe frequency effective for analysis of the iris image is determined as

Fmp=Fsp/2=R/2

[0089] The upper limit Fmp itself is not an effective analysisfrequency. To be precise, therefore, a frequency lower than the upperlimit Fmp may be used for the analysis.

[0090] That is, according to the sampling theorem, even if the analysisis made at a frequency equal to or higher than Fsp/2, a portion of thefeature corresponding to such a frequency is useless as a feature. Ifsuch a portion is included in the entire feature for comparison, theentire degree of coincidence degrades.

[0091] The reason why the sampling frequency is determined with respectto the circumference at the iris/pupil boundary is that the pattern (offolds) extending radially from the innermost radius of the iris (nearthe iris/pupil boundary) works as an effective feature for personalauthentication, and that the polar coordinates are applied to the irisseparated and the Gabor filters are used for detecting a change inshading in the circumferential direction on the polar coordinate system.

[0092] When a normal coordinate system is applied to the iris image forthe analysis in place of the polar coordinate system, the resolution maybe determined from an amount that varies with the size of the irissampled, such as the radius or diameter of the iris image, the area(number of dots) of the iris region separated, and the area (number ofdots) of the pupil, for determination of the analysis frequency band.

[0093] In some terminals used for authentication, the resolution and thelens system of the image capture device incorporated therein are known,and when the image capture distance is roughly constant (the depth offield is small), the size of the iris taken is roughly the same. In thiscase, therefore, the analysis frequency band may be determined inadvance for each terminal using a method as described above. In thiscase, a reference table associating the terminal with the analysisfrequency band may be prepared, to enable determination of the analysisfrequency band from the type of the terminal.

[0094] There is another case that the image capture distance can bemeasured with a distance-measuring sensor and camera information such asthe lens system (zooming available) and the resolution of the imagecapture device can be obtained. In this case, the size of the iris takenis roughly predictable. Therefore, a reference table associating theimage capture distance and the camera information with the analysisfrequency band may be prepared, to enable determination of the analysisfrequency band from the image capture distance and the camerainformation.

[0095] Assume that during the registration, frequency analysis wasperformed for four frequency bands having center frequencies F1 to F4with the frequency F4U as the upper limit, to calculate a feature.

[0096] In the above case, when the upper limit Fmp of the frequency forthe iris image during the authentication is larger than the upper-limitfrequency F4U during the registration, frequency analysis is performedfor the four frequency bands as was done during the registration. WhenFmp<F4U, the maximum n satisfying Fmp≦FnU is calculated. If n=3,frequency analysis for three frequency bands having center frequenciesF1 to F3 is performed.

[0097] In step SB08, a feature is extracted using the Gabor filtersrepresented by expression (1) above. This step is substantially the sameas step SA06 during the registration, except that the frequency analysisis performed for the frequency bands determined in step SB07. When n=3in step SB07, that is, when it has been determined in step SB07 thatanalysis for three frequency bands having center frequencies F1 to F3should be performed, three Gabor filters (ω1=2πF1, ω2=2πF2, ω3=2πF3)corresponding to these frequency bands are used. Extraction of a featurefrom a Gabor-filtered signal is performed using expressions (2) as instep SA06.

[0098] In step SB09, as shown in FIG. 13, sub-features Fsi (binarycodes) corresponding to frequencies Fi, extracted in step SB08, are puttogether to obtain an identification iris code FT.

[0099] Once the above processing is completed in the iris authenticationterminal 15, the generated feature and analysis frequency informationare sent to the iris authentication server 11 via the network 13.

[0100] In this embodiment, the iris authentication terminal 15 executesthe steps until step SB09 for feature extraction, and then sends thefeature to the iris authentication server 11 via the network 13, so thatauthentication is made by the iris authentication server 11.Alternatively, the iris authentication terminal 15 may execute only stepSB01 for iris image capture and send the iris image to the irisauthentication server 11 via the network 13, so that the irisauthentication server 11 can execute steps SB02 to SB09 for featureextraction, as well as steps SB10 and SB11 for identification.

[0101] Alternatively, the iris authentication terminal 15 may executethe series of processing including steps SB01 for iris image capture,steps SB02 to SB09 for feature extraction, and steps SB10 and SB11 foridentification. In this case, the registration iris codes used forcomparison are sent to the iris authentication terminal 15 via thenetwork 13 for comparison at the terminal 15.

[0102] There are one to N authentication and one to one authentication.In one to N authentication, the person to be authenticated does notinform the system of his or her own ID. Therefore, the feature extractedduring the authentication is compared with all reference features in theiris DB 12. The person is authenticated when the similarity to (distancefrom) the most similar reference feature is equal to or more than (lessthan) a predetermined threshold. In one to one authentication, theperson to be authenticated informs the system of his or her own ID. Thefeature extracted during the authentication is compared with thereference feature corresponding to the ID given by the person. Theperson is authenticated when the similarity (distance) therebetween isequal to or more than (less than) a predetermined threshold. Thus, inone to N authentication, when the comparison is performed at the irisauthentication terminal 15, all the features stored in the iris DB 12are sent to the terminal 15 via the network 13.

[0103] In this embodiment, in the iris authentication server 11, theiris code for authentication is compared with the reference iris codesstored in the iris DB 12 for personal authentication. The presentinvention is applicable to both one to N authentication and one to oneauthentication described above. Since they are only different in whetherthe feature is compared with a plurality of features or a singlefeature, the type of authentication is not specifically mentioned in thefollowing description.

[0104] In step SB10, the identification iris code generated in step SB09during the authentication is compared with a registration reference iriscode stored in step SA07 during the registration. In the comparison, asshown in FIG. 14, analysis frequency information attached to both theidentification and registration iris codes is referred to so thatsub-features analyzed at the same frequency are compared with eachother. In the illustrated example, where analysis was performed for fourfrequency bands having center frequencies F1 to F4 during theregistration while it was performed for three frequency bands havingcenter frequencies F1 to F3 during the authentication, sub-features FS1analyzed at the frequency F1, sub-features FS2 analyzed at the frequencyF2, and sub-features FS3 analyzed at the frequency F3 are respectivelycompared with each other.

[0105] For comparison, the hamming distance is used. In addition, asshown in FIG. 15, in calculation of the hamming distance, the featuresare shifted by a same amount to compensate tilting of the face androtational movement of the eyeball itself. The minimum hamming distanceobtained when the shift amount is varied within a predetermined range(range within which rotation of the iris pattern is allowed) isdetermined as the final hamming distance.

[0106] In step SB11, the hamming distance obtained by comparingrespective bits of the features is divided by the number of comparison(total number of bits) and normalized, to obtain an authenticationdistance (authentication score). When the authentication distance isequal to or less than a predetermine threshold, the identity of theperson is authenticated. When it exceeds the predetermined threshold,the person is rejected as being a stranger.

[0107] In this embodiment, the hamming distance (exclusive OR (XOR)) wasused because the generated iris code is a binary value. If the featureis a multilevel value, another distance measure (such as Euclid distanceand normalized correlation) may be used.

[0108] Thus, in this embodiment, personal authentication is possiblewithout degrading the authentication precision even when the iris imageis taken with an image capture device providing a resolution lower thanthat obtained during the registration.

[0109] Embodiment 2

[0110] When the image capture device used during the authentication islow in resolution, no sub-feature for a high-resolution portion inmulti-resolution analysis (no feature from analysis at a frequency equalto or more than the upper-limit frequency Fmp=Fsp/2) is extracted andthus used for comparison. This reduces the total number of bits(information amount) of features used for comparison. Due to thedecrease in information amount, the separability of a comparison scoredistribution for an identical person from that for different persons maybe low, and this may degrade comparison precision.

[0111]FIG. 16 conceptually shows a distribution D1 of authenticationdistances between identical persons and a distribution D2 ofauthentication distances between different persons, obtained bycomparing features in combinations of arbitrary persons. In FIG. 16,when a threshold TH for distinguishing the identical person from otherpersons is set as illustrated, the part of the distribution D2 fordifferent persons smaller in authentication distance than the thresholdTH (hatched part A1) corresponds to a “false accept” part in which adifferent person is mistakenly accepted. Therefore, from a distributionas that shown in FIG. 16, the false acceptance rate (FAR), the rate atwhich different persons are mistakenly accepted, can be trial-calculatedto some extent.

[0112] In this embodiment, therefore, FAR is trial-calculated in advancefor each combination of sub-features Fsi by referring to the iris DB 12.In addition, a reference FAR value, which should be guaranteed, isdetermined. Thus, in the event that the reference FAR fails to besatisfied when the feature FT is obtained with a low-resolution imagecapture device without including a sub-feature analyzed at a highfrequency (without analysis at a high frequency) during theauthentication as shown in Embodiment 1, it is possible to take measuressuch as inhibiting proceeding to the authentication step.

[0113] A false rejection rate (FRR) is another indicator ofauthentication precision. When the image capture device used during theauthentication is low in resolution, the feature of a high-resolutionportion obtained during multi-resolution analysis is not used.Therefore, since the information amount of the feature used forcomparison is small, the distribution D1 for an identical person itselfmay expand, causing debasement in FRR. In FIG. 16, the part of thedistribution D1 for an identical person greater in authenticationdistance than the threshold TH (hatched part A2) corresponds to the“false rejection” part.

[0114]FIG. 17 is a flowchart of processing in a personal authenticationmethod of Embodiment 2 of the present invention. The processing shown inFIG. 17 is executed after the storing of the feature in the iris DB 12(see FIG. 5).

[0115] As in Embodiment 1, an iris image is analyzed with 2D Gaborfilters having a plurality of frequency passbands, to preparesub-features FSi corresponding to respective frequencies Fi. In thisembodiment, assume that four frequency bands are used for analysis asshown in FIG. 12 to obtain four sub-features FSi. This operation isrepeated by the number of persons entered in the iris DB 12. Assumeherein that the number of persons entered in the iris DB 12 is N, atleast one iris image is taken for each person, and a feature isextracted from each iris image.

[0116] In step SA08, the reference FAR (=T) and a feature (combinationof sub-features) used for trial-calculation of FAR are determined.First, suppose all the sub-features FS1 to FS4 are selected. That is,all the sub-features FS1 to FS4 are put together to generate the featureFT. In step SA09, for the feature FT, calculated are the authenticationdistance between features extracted from a person P and theauthentication distances between a feature extracted from the person Pand features extracted from all of the persons other than P. Theresultant distributions are shown in FIG. 18A.

[0117] When there are a plurality of registered features for the personP, an authentication distance distribution DA1 can be prepared as shownin FIG. 18A by comparing features of the identical person P with oneanother. An authentication distance distribution DA2 can also beprepared by comparing the person P with the other persons. A thresholdThA is determined from the distributions DA1 and DA2. Herein, thethreshold ThA is set so that FAR and FRR are equal to each other.

[0118] The threshold ThA is set for each combination of the person P andthe feature, and the setting may be done in various ways depending onthe purpose of the authentication. For example, if it is desired toreject others without fail, the threshold ThA may be set lower tradingoff a more or less increase in false rejection rate (FRR). If rejectionof the identification of the person P will cause much difficulty fromthe standpoint of user interface, the threshold ThA may be set highertrading off the false acceptance rate (FAR).

[0119] If only one registered feature is available for the person P, nodistribution for the identical person is generated. The threshold Th istherefore determined referring to only FAR.

[0120] If the registered data amount in the iris DB 12 is notsufficiently large, the two distributions may not overlap each other,unlike those shown in FIG. 18A. In this case, the two distributions maybe approximated with an appropriate function to form an overlap portionthereof.

[0121] In step SA10, the FAR is trial-calculated using the thresholdThA. In the case shown in FIG. 18A, the FAR corresponds to a hatchedpart AA1. Whether or not the part AA1 is smaller than a predeterminedreference FAR=T is determined. If AA1<T, it is judged that analysis atfrequencies F1 to F4 is possible for identification of the person P.

[0122] Likewise, the FAR in the combination of sub-features FS1 to FS4is trial-calculated for all the persons, and whether or not thetrial-calculated FAR is lower than a predetermined threshold T isjudged. The reason why the FAR is trial-calculated for each person is asfollows. Some persons can be adequately distinguished from others onlywith a feature analyzed at a low frequency while other persons cannot.Therefore, combinations of sub-features suitable for respective personscan be obtained by trial-calculation for each person.

[0123] Persons distinguishable from others only with a feature analyzedat a low frequency are those unique in the low-frequency portion of theiris pattern. Naturally, unlike the method of this embodiment, theauthentication precision may be trial-calculated for an identical personand for different persons to be used for all the persons in common. Inthis case, the entire distribution of authentication distances betweenidentical persons and the entire distribution of authenticationdistances between different persons are compared with each other, totrial-calculate FAR. This eliminates the necessity of holding effectivecombinations of sub-features for respective persons in the iris DB 12,and thus the effect that the memory capacity can be reduced is attained.

[0124] Subsequently, steps SA08 to SA10 are repeated for anothercombination of sub-features. For example, suppose the sub-features FS1to FS3 were selected as the second combination, and as a result ofsimilar processing, authentication with adequate precision wassuccessfully done for all the persons.

[0125] Thereafter, suppose the sub-features FS1 and FS2 are selected asthe third combination. FIG. 18B shows a distribution DB1 ofauthentication distances between the identical persons P and adistribution DB2 of authentication distances between the person P andall of the persons other than the person P obtained in this case. TheFAR corresponds to the area of a hatched part AB1. When AB1≧T, adequateauthentication precision is not obtainable in analysis at frequencies F1and F2 for identification of the person P. Thus, it is found from thecalculation using the iris DB 12 that adequate authentication precisionwill not be obtained for authentication of the person P unless at leastthe frequencies F1 to F3 are used for analysis.

[0126] Therefore, in this embodiment, if the person P attempts to beauthenticated using an iris image with a resolution with which thehighest frequency used for analysis will be lower than F3, the person Pcan be warned or notified of inability of authentication by the irisauthentication server 11.

[0127] In this embodiment, the FAR was used as an indicator ofauthentication precision, which however varies with the setting of thethreshold Th. As an alternative indicator of authentication precisionindependent of the threshold Th, therefore, the sum of FAR and FRR, forexample, may be used. In this case, the authentication precision isindependent of the threshold Th, and determined only by twodistributions as shown in FIG. 16.

[0128] In this embodiment, the distribution for the identical person isused for each person. Actually, however, since the number of registeredfeatures of one person is limited (one to several pieces), thedistribution as described above may not be obtained in some cases. Inthese cases, an iris moving picture may be taken for several secondsduring the registration of the iris image, and frame images of themoving picture may be extracted to be used as registered images. Forexample, when a moving picture of an iris is taken for three secondswith a 30 frames/sec progressive scanning camera, a total of 90 irisimages are obtained. After defective images such as that taken when theperson blinks are removed, the remaining images can be used as imagesfor registration. If a moving picture is taken with an illuminator ofvarying lightness, images with various pupil sizes can be obtained. Inthis case, it is possible to obtain a distribution for the identicalperson using images taken under various conditions.

[0129] The distribution for the identical person can be updated byadding duly-authenticated features to the database every time the personis authenticated.

[0130] Steps SA08 to SA10 are processing performed in advance during theregistration, not during the comparison. In the event that data is newlyentered in the iris DB 12 or a registered feature for a certain personis updated, this processing may be executed again. If execution of theprocessing every time of registration is undesirable, the processing maybe executed periodically such as once a week or a month.

[0131] The combinations of analysis frequencies are not limited to thoseof frequencies in series such as frequencies F1 to Fk, but may becombinations of discrete frequencies such as frequencies F1, F3 and F5.The latter combinations are effective in such cases that use offrequencies F1, F3 and F5 increases uniqueness (distinction from others)but additional use of frequencies F2 and F4 rather decreases distinctionfrom distributions for different persons.

[0132] In this embodiment, whether or not a person should be finallyauthenticated is judged depending on the estimated authenticationprecision. The authentication precision can also be used to control thepower bestowed on the person authenticated after the authentication. Forexample, in personal authentication for electronic commerce, the ceilingof the amount of money allowed for transaction may be set depending onthe estimated authentication precision. In personal authentication forentrance/exit control, the room a person is allowed to enter may bedetermined depending on the estimated authentication precision for theperson. In personal authentication for logging in to a PC or the like,executable commands (browsing only, rewritable, erasable) and the likemay be controlled depending on the estimated authentication precision.

[0133] Whether or not re-authentication is allowed may be determineddepending on the estimated authentication precision. For example, if thecamera of the iris authentication terminal 15 is zooming-adjustable,instruction may be made to take an image again with a larger zoom value.If the camera is focusing-adjustable, instruction may be made to take animage again with the iris being closer to the camera.

[0134] In Embodiment 1, the selection of frequencies during theauthentication was made based on the resolution of the iris image takenduring the authentication. In place of or in addition to the resolution,the selection of frequencies during the authentication may be made basedon the authentication precision for each combination of frequencies. Forexample, among combinations of frequencies providing a FAR equal to orless than a predetermined value, a combination providing the smallestFAR or a combination composed of the smallest number of frequencies maybe used during the authentication.

[0135] In this embodiment, the threshold of the authentication distancewas determined in advance, and the FAR was trial-calculated using thethreshold. In reverse, the threshold can be calculated from the minimumguaranteed FAR. Therefore, if the FAR fails to reach the minimumguaranteed value, the threshold may be made tighter for nextauthentication.

[0136] The authentication precision can be estimated from the featureobtained during the registration as described above. Therefore, it ispossible, for example, to take images of both right and left irises,trial-calculate authentication precision for these images, and registerone of the iris images better in authentication precision (that is, moredistinguishable from the features for others). Alternatively, if bothiris images are low in authentication precision, both of them may beregistered to be used for authentication.

[0137] Likewise, in the case of fingerprint authentication, for example,a plurality of fingers may be registered to finally select a fingerprintwith high authentication precision. Otherwise, if a predetermined levelof authentication precision is not obtainable with a single finger, aplurality of fingers may be registered to be used together duringauthentication.

[0138] Embodiment 3

[0139] In Embodiment 1, an iris image was taken with an image capturedevice providing a comparatively high resolution capable of taking aniris image with an adequate resolution during the registration.Thereafter, multi-resolution analysis was performed to extract asub-feature for each analysis frequency band. During the authentication,at least one frequency band for analysis is determined from the irisimage taken. Sub-features obtained for the same frequency band duringthe registration and during the authentication were compared with eachother, to perform personal authentication.

[0140] In Embodiment 3 of the present invention, a plurality of irisimages with different resolutions are acquired during the registration.

[0141] Such iris images with different resolutions may be acquired byactually taking a plurality of iris images with different resolutions.This can be done using a plurality of cameras of which image captureelements provide different resolutions, or by taking images a pluralityof times with one camera while varying the zoom value of the camera.

[0142] As another acquiring method, an iris image may be taken at anadequate resolution, and, as shown in FIG. 19, the iris image taken maybe subjected to a plurality of (three in FIG. 19) low-pass filters LPF1to LPF3 to generate a plurality of iris images with differentresolutions. In this case, the resultant iris images are the same insize, but different in frequency components contained in the images(high-frequency components are cut).

[0143] Thereafter, frequency bands to be used for analysis aredetermined for the acquired iris images with different resolutions inthe following manner.

[0144] As for the iris images with different resolutions actually taken,the frequency bands are determined based on the number of pixels on thecircumference at the boundary between the iris and the pupil, as wasdone during the authentication in Embodiment 1.

[0145] Suppose, for example, iris images are taken at three resolutionsR1, R2 and R3, and respective upper-limit frequencies Fsp1, Fsp2 andFsp3 for analysis are determined according to the sampling theorem.Also, n(n=1 to 4) Gabor filters each having a passband having a centerfrequency Fn (lower-limit frequency FnL and upper-limit frequency FnU)are prepared as shown in FIG. 20. The Gabor filters are selected so thatthe upper limit FnL of each passband does not exceed a half of thesampling frequency Fs for the iris image. As a result, the image withthe resolution R1 is analyzed using only the Gabor filter 1, the imagewith the resolution R2 is analyzed using the Gabor filters 1 to 3, andthe image with the resolution R3 is analyzed using the Gabor filters 1to 4.

[0146] As in Embodiment 1, the passbands of the adjacent Gabor filtersoverlap each other at positions where the Gaussian function is halved.

[0147] As for the iris images acquired using a plurality of low-passfilters, the frequency bands are determined from the properties of thelow-pass filters used.

[0148]FIGS. 21A to 21D are views of an iris image taken with a cameraand iris images generated using a plurality of low-pass filters,represented in terms of the frequency range using Fourier transform.Note that although 2D Fourier transform is actually used because theimages are two-dimensional, one-dimensional representations (dimensionin the circumferential direction of the iris on polar coordinates) areshown in FIGS. 21A to 21D for simplification of description as inEmbodiment 1.

[0149] From FIGS. 21A to 21D, it is found that the low-pass filter LPFi(i=1 to 3) allows pass of frequencies equal to and lower than Fci.Herein, the following relationship is established:

Fsp/2>Fc1>Fc2>Fc3

[0150] where Fsp is the sampling frequency of the original image. Thefrequency band for analysis can be determined uniquely from theproperties of the low-pass filter used. This will be described morespecifically referring to FIG. 22. Suppose the Gabor filters areselected so that the upper-limit frequency FnU of the passband of eachGabor filter does not exceed the blocking frequency Fci of the low-passfilter used for the image in question. Then, the image subjected to thelow-pass filter LPF1 is analyzed using the Gabor filters 1 to 3, theimage subjected to the low-pass filter LPF2 is analyzed using the Gaborfilters 1 and 2, and the image subjected to the low-pass filter LPF3 isanalyzed using only the Gabor filter 1. The original image is analyzedusing the Gabor filters 1 to 4 as in Embodiment 1.

[0151] Extraction of features is the same as that described inEmbodiment 1 and thus description is omitted here. As shown in FIG. 23,extracted features are stored in iris DBs 12 a to 12 d separately bycombination of analysis frequency bands.

[0152] During the authentication, as in Embodiments 1 and 2, thefrequency bands for analysis are determined from an iris image taken. Acombination of frequency bands identical to those for analysis isselected from the plurality of iris DB 12 a to 12 d shown in FIG. 23,and authentication is performed using the features stored in theselected iris DB in the manner described in Embodiment 1. Details of theauthentication are omitted here.

[0153] By the above processing in this embodiment, substantially thesame effects as those in Embodiment 1 can be obtained.

[0154] In this embodiment, the frequency bands for analysis wereselected based on the upper-limit frequency of information contained inthe image. Alternatively, authentication precision may be estimated inadvance as in Embodiment 2, so that a combination of analysisfrequencies can be selected based on the estimated authenticationprecision. In this case, the analysis frequency bands are not limited tothose adjacent to one another (for example, F1, F2 and F3 in FIG. 22),but may be discrete ones (for example, F1 and F3 in FIG. 22) asdiscussed in Embodiment 2.

[0155] Thus, according to the present invention, degradation inauthentication precision is suppressed even when authentication is doneusing iris images taken with image capture devices providing differentresolutions. In addition, measures suitable for estimated authenticationprecision can be taken even when the information amount of a feature islow.

[0156] While the present invention has been described in a preferredembodiment, it will be apparent to those skilled in the art that thedisclosed invention may be modified in numerous ways and may assume manyembodiments other than that specifically set out and described above.Accordingly, it is intended by the appended claims to cover allmodifications of the invention which fall within the true spirit andscope of the invention.

What is claimed is:
 1. A personal authentication method using biologicalinformation, wherein during registration, acquired biologicalinformation is frequency-analyzed using a plurality of frequencies togenerate a feature for each frequency and register the feature, andwherein the method comprises the steps of: selecting a frequency usedfor frequency analysis for authentication from the plurality offrequencies; performing frequency analysis for acquired biologicalinformation of a person to be authenticated using the selected frequencyto generate a feature for the frequency; and comparing the generatedfeature with the feature generated for the same frequency during theregistration to perform personal authentication.
 2. The method of claim1, wherein the biological information is an image of an iris of an eye.3. The method of claim 2, wherein the selection of the frequency duringthe authentication is performed based on a resolution of an iris imagetaken during the authentication.
 4. The method of claim 3, wherein theresolution of the iris image is determined from the iris image itself.5. The method of claim 4, wherein the resolution of the iris image isdetermined based on the length of a circumference corresponding to theboundary between the iris and the pupil of the iris image.
 6. The methodof claim 3, wherein the resolution of the iris image is determined frominformation on an apparatus with which the iris image was taken.
 7. Themethod of claim 1, wherein the selection of the frequency during theauthentication is performed based on authentication precision for eachcombination of the plurality of frequencies.
 8. The method of claim 7,wherein the authentication precision is calculated using a distributionof authentication scores between identical persons and a distribution ofauthentication scores between different persons.
 9. The method of claim1, wherein the authentication precision during the authentication isestimated from the selected frequency.
 10. The method of claim 9,wherein the authentication precision is estimated using a distributionof authentication distances between identical persons and a distributionof authentication distances between different persons.
 11. The method ofclaim 9, wherein whether or not the person to be authenticated should befinally authenticated is judged according to the estimatedauthentication precision.
 12. The method of claim 9, wherein a right tobe bestowed on the person to be authenticated after the authenticationis controlled according to the estimated authentication precision. 13.The method of claim 9, wherein whether or not re-authentication isperformed is judged according to the estimated authentication precision.14. A personal authentication device using biological information,wherein during registration, acquired biological information isfrequency-analyzed using a plurality of frequencies to generate afeature for each frequency and register the feature, and wherein thedevice comprises: means for selecting a frequency used for frequencyanalysis for authentication from the plurality of frequencies; means forperforming frequency analysis for acquired biological information of aperson to be authenticated using the selected frequency to generate afeature for the frequency; and means for comparing the generated featurewith the feature generated for the same frequency during theregistration to perform personal authentication.
 15. The device of claim14, wherein the biological information is an image of an iris of an eye.